Friday, March 14

1:30 PM-3:30 PMNicollet D2

MS4
Recent Progress in Sparse Direct Methods

The robust solution of large, sparse linear systems requires efficient direct solvers which employ some form of sparse matrix factorization (Cholesky, LU, or QR). The central idea in sparse direct solution is that of limiting "fill" (zeros in the original matrix that become nonzero in the factor) by ordering the rows and columns of the matrix. The actual numeric factorization is performed in a subsequent step using the reordered matrix.

The solution of sparse linear systems is the main computation in a vast number of scientific and engineering applications. Parallel sparse direct solution is therefore of significant interest to application developers. Furthermore, the development of parallel sparse direct solvers involves new graph algorithms
and data-partitioning techniques and should be of interest to those concerned with algorithm design for parallel unstructured computation.